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Neuroimage Clin. 2014 Dec 13;7:258-65. doi: 10.1016/j.nicl.2014.12.005. eCollection 2015.

Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease.

Author information

1
Department of Mechanical, Aerospace and Biomedical Engineering, Knoxville, TN 37996, USA.
2
Department of Mechanical, Aerospace and Biomedical Engineering, Knoxville, TN 37996, USA ; National Institute of Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN 37996, USA.
3
Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, USA.
4
Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA ; Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA.
5
Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA ; Department of Statistics, University of Kentucky College of Medicine, Lexington, KY, USA.
6
Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA ; Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY 40356, USA.

Abstract

Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

KEYWORDS:

Causality analysis; EEG-based diagnosis; Early Alzheimer's disease; Mild cognitive impairment

PMID:
25610788
PMCID:
PMC4300018
DOI:
10.1016/j.nicl.2014.12.005
[Indexed for MEDLINE]
Free PMC Article

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